Edson Cataldo

Timoshenko beam with uncertainty on the boundary conditions

In mechanical system modeling, uncertainties are present and, to improve the predictability of the models, they should be taken into account. This work discusses uncertainties present in boundary conditions using the model of a vibrating Timoshenko beam, free in one end and pinned with rotation constrained by a linear elastic torsional spring in the other end. The Finite Element Method is used to discretize the system and two probabilistic approaches are considered to model the uncertainties: (1) the stiffness of the torsional spring is taken as uncertain and a random variable is associated to it (parametric probabilistic approach); (2) the whole stiffness matrix is considered as uncertain and a probabilistic model is constructed for the associated random matrix (nonparametric probabilistic approach). In both approaches, the probability density functions are deduced from the Maximum Entropy Principle. In the first approach only the uncertainty of a parameter is taken into account, and in the second approach, the uncertainties of the model are taken into account, globally. Both approaches are compared and their capability to improve the predictability of the system response is discussed.

Comparing Two Strategies to Model Uncertainties in Structural Dynamics

In the modeling of dynamical systems, uncertainties are present and they must be taken into account to improve the prediction of the models. Some strategies have been used to model uncertainties and the aim of this work is to discuss two of those strategies and to compare them. This will be done using the simplest model possible: a two d.o.f. (degrees of freedom) dynamical system. A simple system is used because it is very helpful to assure a better understanding and, consequently, comparison of the strategies. The first strategy (called parametric strategy) consists in taking each spring stiffness as uncertain and a random variable is associated to each one of them. The second strategy (called nonparametric strategy) is more general and considers the whole stiffness matrix as uncertain, and associates a random matrix to it. In both cases, the probability density functions either of the random parameters or of the random matrix are deduced from the Maximum Entropy Principle using only the available information. With this example, some important results can be discussed, which cannot be assessed when complex structures are used, as it has been done so far in the literature. One important element for the comparison of the two strategies is the analysis of the samples spaces and the how to compare them.

Analysis and Classification of Voice Pathologies Using Glottal Signal Parameters

The classification of voice diseases has many applications in health, in diseases treatment, and in the design of new medical equipment for helping doctors in diagnosing pathologies related to the voice. This work uses the parameters of the glottal signal to help the identification of two types of voice disorders related to the pathologies of the vocal folds: nodule and unilateral paralysis. The parameters of the glottal signal are obtained through a known inverse filtering method, and they are used as inputs to an Artificial Neural Network, a Support Vector Machine, and also to a Hidden Markov Model, to obtain the classification, and to compare the results, of the voice signals into three different groups: speakers with nodule in the vocal folds; speakers with unilateral paralysis of the vocal folds; and speakers with normal voices, that is, without nodule or unilateral paralysis present in the vocal folds. The database is composed of 248 voice recordings (signals of vowels production) containing samples corresponding to the three groups mentioned. In this study, a larger database was used for the classification when compared with similar studies, and its classification rate is superior to other studies, reaching 97.2%.

Evolving Spiking Neural Networks for Recognition of Aged Voices

The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. This is a relevant problem to those people who use their voices professionally, and its early identification can help determine a suitable treatment to avoid its progress or even to eliminate the problem. This work focuses on the development of a new model for the identification of aging voices (independently of their chronological age), using as input attributes parameters extracted from the voice and glottal signals. The proposed model, named Quantum binary-real evolving Spiking Neural Network (QbrSNN), is based on spiking neural networks (SNNs), with an unsupervised training algorithm, and a Quantum-Inspired Evolutionary Algorithm that automatically determines the most relevant attributes and the optimal parameters that configure the SNN. The QbrSNN model was evaluated in a database composed of 120 records, containing samples from three groups of speakers. The results obtained indicate that the proposed model provides better accuracy than other approaches, with fewer input attributes.

Voice Signals Produced With Jitter Through a Stochastic One-mass Mechanical Model

BACKGROUND The quasiperiodic oscillation of the vocal folds causes perturbations in the length of the glottal cycles, which are known as jitter. The observation of the glottal cycles variations suggests that jitter is a random phenomenon described by random deviations of the glottal cycle lengths in relation to a corresponding mean value and, in general, its values are expressed as a percentage of the duration of the glottal pulse. OBJECTIVE The objective of this paper is the construction of a stochastic model for jitter using a one-mass mechanical model of the vocal folds, which assumes complete right-left symmetry of the vocal folds, and which considers motions of the vocal folds only in the horizontal direction. STUDY DESIGN The jitter has been the subject for researchers due to its important applications such as the identification of pathological voices (nodules in the vocal folds, paralysis of the vocal folds, or even, the vocal aging, among others). Large values for jitter variations can indicate a pathological characteristic of the voice. METHOD The corresponding stiffness of each vocal fold is considered as a stochastic process, and its modeling is proposed. RESULTS The probability density function of the fundamental frequency related to the voice signals produced are constructed and compared for different levels of jitter. Some samples of synthesized voices in these cases are obtained. CONCLUSIONS It is showed that jitter could be obtained using the model proposed. The Praat software was also used to verify the measures of jitter in the synthesized voice signals.

A brief review and a new treatment for rigid bodies collision models

In general the motion of a body takes place in a confined environment and collision of the body with the containing wall is possible. In order to predict the dynamics of a body in this condition one must know what happens in a collision. Therefore, the problem is: if one knows the pre-collision dynamics of the body and the properties of the body and the wall one wants to predict the post-collision dynamics. This problem is quite old and it appeared in the literature in 1668. Up to 1984 it seemed that Newtons model was enough to solve the problem. But it was found that this was not the case and a renewed interest in the problem appeared. The aim of this paper is to treat the problem of plan collisions of rigid bodies, to classify the different models found in the literature and to present a new model that is a generalization of most of these models.

Jitter generation in voice signals produced by a two-mass stochastic mechanical model

Jitter is a phenomenon caused by the perturbation in the length of the glottal cycles due to the quasi-periodic oscillation of the vocal folds in the production of the voice. It can be modeled as a random phenomenon described by the deviations of the glottal cycle length in relation to a mean value. Its study has been developed due to important applications such as aid in identification of voices with pathological characteristics, when its values are large, because a normal voice has naturally a low level of jitter. The aim of this paper is to construct a stochastic model of jitter using a two-mass mechanical model of the vocal folds, assuming complete right-left symmetry of the vocal folds and considering the motion of the vocal folds only in the horizontal direction. The stiffnesses taken into account in the model are considered as stochastic processes and their modeling are proposed. Glottal signals and voice signals are generated with jitter and the probability density function of the fundamental frequency is constructed for several values of the hyperparameters that control the level of jitter.

The Relevance Vector Machine Applied to the Modeling of Wireless Channels

A good modeling of the radio propagation channel is essential to the design of high-performance wireless systems; therefore, the proper interpretation of the data acquired from the sounding process is a task of major importance in the construction of such models. The relevance vector machine (RVM) constitutes a learning algorithm, based on Bayesian Statistics, used in regression and classification problems. Recently, RVM was employed to filter the channel multipath components of simulated power delay profiles embedded with noise, enabling the determination of the paths arriving at the reception antenna, their arrival times and complex amplitudes. In this paper, the RVM algorithm is further studied, regarding its detection capabilities, but the power delay profiles were obtained from measurements carried out in an indoor channel. A comparison with the constant false alarm rate (CFAR) multipath identification scheme, based on computational simulation and real channel measurements, evidences the behavior of both detection schemes. Simulations also present the detection limits of the method, such as maximum multipath magnitude ratio and minimum interarrival time. Finally, the characterization of important parameters of a real wideband multipath indoor channel is presented, in terms of confidence intervals and probability distribution fittings.

Artificial neural networks applied to the estimation of random variables associated to a two-mass model for the vocal folds

The aim of this article is to use artificial neural networks (ANNs) to solve a stochastic inverse problem related to a model for voice production. Three parameters of the model are considered uncertain and random variables are associated to these parameters. For each random variable, a probability density function is constructed using the Maximum Entropy Principle. Substituting the three uncertain parameters for the associated random variables, the new model constructed is stochastic and its output is a stochastic process consisting of realizations of voice signals. The proposed inverse problem consists in mapping the three random variables from the voice signals and the use of ANNs to construct the solution of the inverse problem. Features are extracted from the output voice signals and taken as inputs of the designed ANN, whose outputs are random variables. The probability density functions of these random outputs are estimated and compared with the original ones. Two kinds of problems are discussed. At first, the same probability distribution is used to generate the voice signals and to solve the corresponding inverse stochastic problem. In this case, the actual probability density functions are very well fitted by the simulated ones. Then, different probability density functions are used to generate the voice signals to be used to train the ANN, and to solve the corresponding inverse problem. A good surprise appears: the quality of the estimation is almost unchanged, except for one of the random variables.

Quantum-Inspired Features and Parameter Optimization of Spiking Neural Networks for a Case Study from Atmospheric☆

Abstract Identified cluster of atmospheric discharges, sufficiently near from transmissions line, could be an important alarm to support real time decisions. Lightning are important events that affect the electrical power system operation, which are often responsible for transmission lines outages, and can trigger a sequence of events that lead to system collapse. The Brazilian lightning network detection monitors nearly 18 million events monthly and all this data must be processed and analyzed. This paper uses a hybrid model named the Quantum binary-real evolving Spiking Neural Network (QbrSNN) for clustering problem, where the features and parameters of a spiking neural network (SNN) are optimized using the Quantum-Inspired Evolutionary Algorithm with representation Binary-Real (QIEA-BR). The proposed model is applied to atmospheric discharges data, with a significantly higher clustering accuracy than traditional techniques.